The relationship between anxiety, depression and hopelessness among nonclinical sample

2016 ◽  
Vol 33 (S1) ◽  
pp. S156-S156
Author(s):  
T. Alali
Keyword(s):  
Factor Analysis ◽  
Common Factor ◽  
Strong Relationship ◽  
Beck Anxiety Inventory ◽  
Oblique Rotation ◽  
The Common ◽  
Nonclinical Sample ◽  
Competing Interest ◽  
Anxiety Depression ◽  
The Relationship

IntroductionThis research aims at examining the relationship between anxiety, depression, and hopelessness among nonclinical Kuwaiti sample using Beck Anxiety Inventory, Beck Depression, and hopelessness inventories.Objectiveshighlighting the relationship between anxiety, depression, and hopelessness among nonclinical sample of females and males and the common factor/s.MethodsThe participants were 616 (308 females & 308 males), Kuwait University students. The two genders were matched in age (18.15 ± 0.36 & 18.18 ± 0.38, t = 0.94, P > .05) and BMI (24.12 ± 3.27 & 23.50 ± 4.85, t = 0.54, P > 0.5). The Arabic versions of the Beck Anxiety Inventory (BAI), Beck Depression Inventory-II (BDI-II), the Beck Hopelessness Scale (BHS), and demographic surveys were administered to participants during classes. All participants read and signed a consent form before participating. The correlation matrices, exploratory factor analysis, and reliability analysis are used in this study.ResultsInternal consistency of scores were satisfactory for the BAI, BDI-II, & BHS inventories respectively (Cronbach's alpha (M) = 0.88, 0.75, 0.74 & (F) = 0.89, 0.84, 0.88). A correlation of (r = 0.53) between the BAI and BDI-II and (r = 0.43) with BHS. Meanwhile a correlation of (r = 0.58) between BDI-II & BHS. A principal-axis factor analysis with oblique rotation suggested one factor accounting for 67.73% of the common variance.ConclusionThe results indicate that there is a strong relationship between anxiety, depression and hopelessness. This highlights the important of examining common factors between anxiety, depression and hopelessness among nonclinical sample.Disclosure of interestThe author has not supplied his/her declaration of competing interest.

Relationship of suicide ideation with depression and hopelessness

2016 ◽  
Vol 33 (S1) ◽  
pp. S597-S597 ◽  
Author(s):  
R. Alsalman ◽  
B. Alansari
Keyword(s):  
Factor Analysis ◽  
Suicide Ideation ◽  
Arab World ◽  
Strong Relationship ◽  
Oblique Rotation ◽  
Kuwait University ◽  
Principal Axis Factor Analysis ◽  
Competing Interest ◽  
Demographic Surveys ◽  
Relationship Of

IntroductionThe association between suicide ideation, depression, and hopelessness is relatively ignored in the literature of the Arab World, particularly using suicide ideation, Beck Depression, and hopelessness inventories.ObjectiveThe specific research questions related to this model are as follows: does the relationship between suicide ideation, depression, and hopelessness, postulate the latent factor?MethodsThe participants were 200 girls, first year Kuwait University students. The mean age (18.18 ± 0.38) and BMI (23.50 ± 4.85). The Arabic versions of the Beck Scale for Suicide Ideation (BSI), Beck Depression Inventory-II (BDI-II), the Beck Hopelessness Scale (BHS), and demographic surveys were administered to participants in the class. All participants read and signed a consent form before test administration. The correlation matrices, exploratory factor analysis, and reliability analysis are used in this study.ResultsInternal consistency of scores were satisfactory for the BSI, BDI-II, & BHS inventories respectively (Cronbach's alpha = .91, .89, .85). A correlation of (r = .53) between the BSI and BDI-II and (r = .43) with BHS. Meanwhile, a correlation of (r = .58) between BDI-II & BHS. A principal-axis factor analysis with oblique rotation suggested one factor accounting for 67.73% of the common variance.ConclusionThis trend indicates there is a strong relationship of suicide ideation with depression and hopelessness. The results of the present study suggest that targeting depression may be as important in adolescents as in adults to reduce suicidal ideation and prevent suicidal attempts.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Dimensions of Jewish-American Identity

1973 ◽  
Vol 33 (3) ◽  
pp. 891-900 ◽  
Author(s):  
Itai Zak
Keyword(s):  
United States ◽  
Factor Analysis ◽  
Test Scores ◽  
Common Factor ◽  
The United States ◽  
American Identity ◽  
Jewish American ◽  
Cast Doubt ◽  
The Common ◽  
High Degree

The main problem posed in this study is: What are the content and structure of Jewish and American identity? The Jewish-American Identity Scale, which was adapted and refined for this study, was administered in 1971 to four samples, totaling 1006 Jewish-American college students from various parts of the United States. Initially, factor analysis was applied to the separate samples. Intersample comparisons of factor structures indicated a high degree of congruency; consequently, the samples were combined for subsequent analyses. Factor analysis of the test scores demonstrated that most of the common factor variance was appropriated by two relatively orthogonal factors. Items dealing with American identity and those dealing with Jewish identity had medium to high loadings on the two respective factors. These findings supported the hypothesis of the duality and the orthogonality of dimensions of Jewish and American identity, and cast doubt on the notion forwarded by some researchers that Jewish-American identity forms a bipolar continuum.

Exploratory Factor Analysis

2018 ◽  
Author(s):  
Brian D. Haig
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Common Factor ◽  
Methodological Issues ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Scientific Inference ◽  
Confirmatory Factor ◽  
The Common

Chapter 6 argues that exploratory factor analysis is an abductive method of theory generation that exploits a principle of scientific inference known as the principle of the common cause. Factor analysis is an important family of multivariate statistical methods that is widely used in the behavioral and social sciences. The best known model of factor analysis is common factor analysis, which has two types: exploratory factor analysis and confirmatory factor analysis. A number of methodological issues that arise in critical discussions of exploratory factor analysis are considered. It is suggested that exploratory factor analysis can be profitably employed in tandem with confirmatory factor analysis.

scholarly journals An Entropy-Based Approach for Measuring Factor Contributions in Factor Analysis Models

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 634 ◽  
Author(s):  
Nobuoki Eshima ◽  
Minoru Tabata ◽  
Claudio Giovanni Borroni
Keyword(s):  
Factor Analysis ◽  
Conventional Method ◽  
Latent Variables ◽  
Common Factor ◽  
Present Approach ◽  
Factor Loadings ◽  
Numerical Example ◽  
Manifest Variable ◽  
The Common ◽  
Analysis Models

In factor analysis, factor contributions of latent variables are assessed conventionally by the sums of the squared factor loadings related to the variables. First, the present paper considers issues in the conventional method. Second, an alternative entropy-based approach for measuring factor contributions is proposed. The method measures the contribution of the common factor vector to the manifest variable vector and decomposes it into contributions of factors. A numerical example is also provided to demonstrate the present approach.

scholarly journals Exploring Differences in Pain Beliefs Within and Between a Large Nonclinical (Workplace) Population and a Clinical (Chronic Low Back Pain) Population Using the Pain Beliefs Questionnaire

2013 ◽  
Vol 93 (12) ◽  
pp. 1615-1624 ◽  
Author(s):  
Andrew J. Baird ◽  
Roger A. Haslam
Keyword(s):  
Factor Analysis ◽  
Low Back Pain ◽  
Back Pain ◽  
Cross Sectional Study ◽  
Low Back ◽  
Sectional Study ◽  
Cross Sectional ◽  
Pain Beliefs ◽  
Nonclinical Sample ◽  
The Relationship

Background Beliefs, cognitions, and behaviors relating to pain can be associated with a range of negative outcomes. In patients, certain beliefs are associated with increased levels of pain and related disability. There are few data, however, showing the extent to which beliefs of patients differ from those of the general population. Objective This study explored pain beliefs in a large nonclinical population and a chronic low back pain (CLBP) sample using the Pain Beliefs Questionnaire (PBQ) to identify differences in scores and factor structures between and within the samples. Design This was a cross-sectional study. Methods The samples comprised patients attending a rehabilitation program and respondents to a workplace survey. Pain beliefs were assessed using the PBQ, which incorporates 2 scales: organic and psychological. Exploratory factor analysis was used to explore variations in factor structure within and between samples. The relationship between the 2 scales also was examined. Results Patients reported higher organic scores and lower psychological scores than the nonclinical sample. Within the nonclinical sample, those who reported frequent pain scored higher on the organic scale than those who did not. Factor analysis showed variations in relation to the presence of pain. The relationship between scales was stronger in those not reporting frequent pain. Limitations This was a cross-sectional study; therefore, no causal inferences can be made. Conclusions Patients experiencing CLBP adopt a more biomedical perspective on pain than nonpatients. The presence of pain is also associated with increased biomedical thinking in a nonclinical sample. However, the impact is not only on the strength of beliefs, but also on the relationship between elements of belief and the underlying belief structure.

Explanatory and Confirmatory Factor Structure of Beck Anxiety Inventory in College Sample

2017 ◽  
Vol 41 (S1) ◽  
pp. S108-S108
Author(s):  
S. Kareemi ◽  
B.M. Alansari
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Clinical Sample ◽  
Self Report ◽  
Beck Anxiety Inventory ◽  
College Sample ◽  
Two Samples ◽  
Confirmatory Factor ◽  
Males And Females ◽  
Competing Interest

IntroductionThe Beck anxiety inventory (BAI) is a widely used 21-item self-report inventory used to assess anxiety levels in adults and adolescents in both clinical and non-clinical populations. The values for each item are summed yielding an overall or total score for all 21 symptoms that can range between 0 and 63 points. A total score of 0–7 is interpreted as a “Minimal” level of anxiety; 8–15 as “Mild”; 16–25 as “Moderate”, and; 26–63 as “Severe”. There is no study until this date that examines the Explanatory and confirmatory factor structure factor structure of BAI in college student in Kuwaiti.ObjectivesThe current study investigated the original four-factor structure of the (BAI) in non-clinical sample of college students.MethodsSample one consisted of 540 males and females while sample two consisted of 600 males and females from Kuwait University undergraduates. The Arabic version of BAI was administered to participants. Explanatory factor analysis based on sample one and conformity factor analysis based on sample 2.ResultsThe results revealed four factor structures of BAI in the two samples of Kuwaiti students. Which included neurophysiological, subjective, autonomic, and panic factors.ConclusionsThe results of both confirmatory and exploratory factor analysis indicated that the original four-factor structures of the BAI do provide the best fit for the college sample.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals An Entropy-Based Tool to Help the Interpretation of Common-Factor Spaces in Factor Analysis

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 140
Author(s):  
Nobuoki Eshima ◽  
Claudio Giovanni Borroni ◽  
Minoru Tabata ◽  
Takeshi Kurosawa
Keyword(s):  
Factor Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation ◽  
Common Factor ◽  
Common Factors ◽  
Correlation Coefficients ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Numerical Examples ◽  
The Common

This paper proposes a method for deriving interpretable common factors based on canonical correlation analysis applied to the vectors of common factors and manifest variables in the factor analysis model. First, an entropy-based method for measuring factor contributions is reviewed. Second, the entropy-based contribution measure of the common-factor vector is decomposed into those of canonical common factors, and it is also shown that the importance order of factors is that of their canonical correlation coefficients. Third, the method is applied to derive interpretable common factors. Numerical examples are provided to demonstrate the usefulness of the present approach.

Research on Blind Source Separation with Noise Based on Multi Factor Analysis

2014 ◽  
Vol 644-650 ◽  
pp. 3947-3950
Author(s):  
Guo Hua Liu ◽  
Zeng Shan Yin ◽  
Zheng Wei Wang ◽  
Xiao Song Yao ◽  
Wen Zhe Yang
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Blind Source Separation ◽  
Random Error ◽  
Common Factor ◽  
Source Separation ◽  
Processing Method ◽  
Fastica Algorithm ◽  
Multifactor Analysis ◽  
The Common

The pre-processing method of blind source separation based on multifactor analysis is proposed to solve the blind source with noise. Firstly, the shortcomings of existing methods of blind source separation are point out after analyzing their principles. The multifactor analysis is introduced in blind source separation and the maximum likelihood estimate based on expectation maximum is used to estimate the common factor and random error. Finally the FastICA algorithm is used to separate BSS result. The validity and the advantage of this method are illustrated by an example.

Structure of Ethnic Identity of Arab-Israeli Students

1976 ◽  
Vol 38 (1) ◽  
pp. 239-246 ◽  
Author(s):  
Itai Zak
Keyword(s):  
Factor Analysis ◽  
Ethnic Identity ◽  
Common Factor ◽  
Self Esteem ◽  
American Identity ◽  
The Other ◽  
Jewish American ◽  
Self Identity ◽  
The Common ◽  
Arab Minority

The main problems posed in this study were: What is the structure of the Arab-Israeli identity? What is the relation of ethnic identity to another self-referent scale? The Ethnic Identity and Self-esteem scales were administered in the summer of 1973–74 to 532 Arab-Israeli university students. Factor analysis of the items demonstrated that most of the common factor variance was appropriated by three factors, all of which were clearly recognized as already known constructs. Despite the peculiar situation of the Arab minority in Israel, two relatively orthogonal factors, an Arab identity and an Israeli identity, emerged while the other factor represented a self-esteem construct. These findings supported the conclusions of previous research on Jewish-American identity and raised questions about the notion that ethnic-majority identity forms a bipolar continuum. The ethnic identity is then discussed in relation to the broader concept of self-identity.

Sign in / Sign up

Export Citation Format

Share Document